Date: Thu, 28 Mar 2024 19:43:40 +0100 (CET)
Message-ID: <657887356.211.1711651420329@vm-confFLEXnet>
Subject: Exported From Confluence
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The application monitors loan contracts. The key parameter =
for early warning is the probability of default calculated in a neuronal network (PD).
The PD is accompanied by the expected credit loss (ECL), also calculated=
in a neuronal network.
The PD triggers an early warning in case it exceeds entirely or its vari=
ance exceeds a specific threshold. The ECL helps to qualify the urgency of =
forbearance or modification.
Depending on the configuration in question, early monitoring can be a re=
cord in a watch list or task list.
Figure: Early warning
The early warning view can be used for several purposes:
1. Identifying early warnings
Early warnings are listed in a watch list or task list. Filters can be s=
et, e.g. the view can be limited to those deals with 'days past due > 0 =
or for combinations of thresholds for PD and ECL.
Workflows can be triggered that require specific actions in loan managem=
ent (e.g. a specific work unit in the lending department receives a task to=
manage forbearance or modifications).
2. Period-based analysis
- identifies a trend for PD and ECL that might be relevant for the approp=
riate EWS action
- understands the impact of former modifications to PD and ECL
The conventional approach for early warning only often works with overdu=
e payment dates (days past due). Then, unfortunately, it is often too late =
to modify and rescue the contract as the loan is usually already in default=
. At this point, the machine learning approach analyses the possible i=
nfluences much more extensively and provides alerts before a payment date b=
ecomes overdue. In addition, machine learning takes into account not only c=
ustomer and contract data but also macro- and microeconomic factors th=
at naturally influence payment behaviour.
The EWS application initiates a workflow when certain events occur. The =
events could also be the variance of the ECL, for example. The workflow act=
ions could also be linked to contract deadlines in such a way that realisti=
c options for action also exist.
Conventional segmentation and staging is linked to conservative models t=
o derive PD, LGD at portfolio level and to calculate ECLs for regulato=
ry purposes. Calibration is =E2=80=9Emanual work=E2=80=9C and hence a =
source of errors. The prediction accuracy depends directly =
on the quality of the manual work during segmentation and calibration. =
;This approach might not be sufficient for the very fine-granular decision =
of an early warning system. If in this case, specific actions should b=
e triggered at individual customer level to reduce potential future losses =
caused by deterioration in credit quality, a more finely granular anal=
ysis is recommended that takes the specific sensitivity of an entity/custom=
er relation into account.
Therefore, an EWS should apply machine learning to identify compara=
ble transactions that have ultimatley failed in the past after showing=
a deterioration in credit quality.
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